Screening for Group A Streptococcal Disease via Solid-State Nanopore Detection of PCR Amplicons
Bibliographic record
Abstract
Single-molecule detection methods are becoming increasingly important for diagnostic applications. Practical early detection of disease requires sensitivity down to the level of single copies of the targeted biomarkers. Of the candidate technologies that can address this need, solid-state nanopores show great promise as digital sensors for single-molecule detection. Here, we present work detailing the use of solid-state nanopores as downstream sensors for a polymerase chain reaction (PCR)-based assay targeting group A streptococcus (strep A), which can be readily extended to detect any pathogen that can be identified with a short nucleic acid sequence. We demonstrate that with some simple modifications to the standard PCR reaction mixture, nanopores can be used to reliably identify strep A in clinical samples. We also discuss methodological best practices for both adapting PCR-based assays to solid-state nanopore readout and analytical approaches by which to decide on sample status.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".